"""Provides partitioning with automatic file-type detection."""

from __future__ import annotations

import copy
import importlib
import io
from typing import IO, Any, Callable, Optional

import requests
from typing_extensions import TypeAlias

from unstructured.documents.elements import DataSourceMetadata, Element
from unstructured.file_utils.filetype import (
    detect_filetype,
    is_json_processable,
    is_ndjson_processable,
)
from unstructured.file_utils.model import FileType
from unstructured.logger import logger
from unstructured.partition.common import UnsupportedFileFormatError
from unstructured.partition.common.common import exactly_one
from unstructured.partition.common.lang import check_language_args
from unstructured.partition.utils.constants import PartitionStrategy
from unstructured.utils import dependency_exists

Partitioner: TypeAlias = Callable[..., list[Element]]


def partition(
    filename: Optional[str] = None,
    *,
    file: Optional[IO[bytes]] = None,
    encoding: Optional[str] = None,
    content_type: Optional[str] = None,
    url: Optional[str] = None,
    headers: dict[str, str] = {},
    ssl_verify: bool = True,
    request_timeout: Optional[int] = None,
    strategy: str = PartitionStrategy.AUTO,
    skip_infer_table_types: list[str] = ["pdf", "jpg", "png", "heic"],
    ocr_languages: Optional[str] = None,  # changing to optional for deprecation
    languages: Optional[list[str]] = None,
    detect_language_per_element: bool = False,
    pdf_infer_table_structure: bool = False,
    extract_images_in_pdf: bool = False,
    extract_image_block_types: Optional[list[str]] = None,
    extract_image_block_output_dir: Optional[str] = None,
    extract_image_block_to_payload: bool = False,
    data_source_metadata: Optional[DataSourceMetadata] = None,
    metadata_filename: Optional[str] = None,
    hi_res_model_name: Optional[str] = None,
    model_name: Optional[str] = None,  # to be deprecated
    starting_page_number: int = 1,
    **kwargs: Any,
) -> list[Element]:
    """Partitions a document into its constituent elements.

    Uses libmagic to determine the file's type and route it to the appropriate partitioning
    function. Applies the default parameters for each partitioning function. Use the document-type
    specific partitioning functions if you need access to additional kwarg options.

    Parameters
    ----------
    filename
        A string defining the target filename path.
    file
        A file-like object using "rb" mode --> open(filename, "rb").
    encoding
        The character-encoding used to decode the input bytes when drawn from `filename` or `file`.
        Defaults to "utf-8".
    url
        The url for a remote document. Pass in content_type if you want partition to treat
        the document as a specific content_type.
    headers
        The headers to be used in conjunction with the HTTP request if URL is set.
    ssl_verify
        If the URL parameter is set, determines whether or not partition uses SSL verification
        in the HTTP request.
    request_timeout
        The timeout for the HTTP request if URL is set. Defaults to None meaning no timeout and
        requests will block indefinitely.
    content_type
        A string defining the file content in MIME type
    metadata_filename
        When file is not None, the filename (string) to store in element metadata. E.g. "foo.txt"
    strategy
        The strategy to use for partitioning PDF/image. Uses a layout detection model if set
        to 'hi_res', otherwise partition simply extracts the text from the document
        and processes it.
    skip_infer_table_types
        The document types that you want to skip table extraction with.
    languages
        The languages present in the document, for use in partitioning and/or OCR. For partitioning
        image or pdf documents with Tesseract, you'll first need to install the appropriate
        Tesseract language pack. For other partitions, language is detected using naive Bayesian
        filter via `langdetect`. Multiple languages indicates text could be in either language.
        Additional Parameters:
            detect_language_per_element
                Detect language per element instead of at the document level.
    pdf_infer_table_structure
        Deprecated! Use `skip_infer_table_types` to opt out of table extraction for any document
        type.
        If True and strategy=hi_res, any Table Elements extracted from a PDF will include an
        additional metadata field, "text_as_html," where the value (string) is a just a
        transformation of the data into an HTML <table>.
        The "text" field for a partitioned Table Element is always present, whether True or False.
    extract_images_in_pdf
        Only applicable if `strategy=hi_res`.
        If True, any detected images will be saved in the path specified by
        'extract_image_block_output_dir' or stored as base64 encoded data within metadata fields.
        Deprecation Note: This parameter is marked for deprecation. Future versions will use
        'extract_image_block_types' for broader extraction capabilities.
    extract_image_block_types
        Only applicable if `strategy=hi_res`.
        Images of the element type(s) specified in this list (e.g., ["Image", "Table"]) will be
        saved in the path specified by 'extract_image_block_output_dir' or stored as base64
        encoded data within metadata fields.
    extract_image_block_to_payload
        Only applicable if `strategy=hi_res`.
        If True, images of the element type(s) defined in 'extract_image_block_types' will be
        encoded as base64 data and stored in two metadata fields: 'image_base64' and
        'image_mime_type'.
        This parameter facilitates the inclusion of element data directly within the payload,
        especially for web-based applications or APIs.
    extract_image_block_output_dir
        Only applicable if `strategy=hi_res` and `extract_image_block_to_payload=False`.
        The filesystem path for saving images of the element type(s)
        specified in 'extract_image_block_types'.
    hi_res_model_name
        The layout detection model used when partitioning strategy is set to `hi_res`.
    model_name
        The layout detection model used when partitioning strategy is set to `hi_res`. To be
        deprecated in favor of `hi_res_model_name`.
    starting_page_number
        Indicates what page number should be assigned to the first page in the document.
        This information will be reflected in elements' metadata and can be be especially
        useful when partitioning a document that is part of a larger document.
    """
    exactly_one(file=file, filename=filename, url=url)

    kwargs.setdefault("metadata_filename", metadata_filename)

    if pdf_infer_table_structure:
        logger.warning(
            "The pdf_infer_table_structure kwarg is deprecated. Please use skip_infer_table_types "
            "instead."
        )

    languages = check_language_args(languages or [], ocr_languages)

    if url is not None:
        file, file_type = file_and_type_from_url(
            url=url,
            content_type=content_type,
            headers=headers,
            ssl_verify=ssl_verify,
            request_timeout=request_timeout,
        )
    else:
        if headers != {}:
            logger.warning(
                "The headers kwarg is set but the url kwarg is not. "
                "The headers kwarg will be ignored.",
            )
        file_type = detect_filetype(
            file_path=filename,
            file=file,
            encoding=encoding,
            content_type=content_type,
            metadata_file_path=metadata_filename,
        )

    if file is not None:
        file.seek(0)

    # avoid double specification of infer_table_structure; this can happen when the kwarg passed
    # into a partition function, e.g., partition_email is reused to partition sub-elements, e.g.,
    # partition an image attachment buy calling partition with the kwargs. In that case here kwargs
    # would have a infer_table_structure already
    kwargs_infer_table_structure = kwargs.pop("infer_table_structure", None)
    infer_table_structure = (
        kwargs_infer_table_structure
        if kwargs_infer_table_structure is not None
        else decide_table_extraction(
            file_type,
            skip_infer_table_types,
            pdf_infer_table_structure,
        )
    )

    partitioner_loader = _PartitionerLoader()

    # -- extracting this post-processing to allow multiple exit-points from function --
    def augment_metadata(elements: list[Element]) -> list[Element]:
        """Add some metadata fields to each element."""
        for element in elements:
            element.metadata.url = url
            element.metadata.data_source = data_source_metadata
            if content_type is not None:
                out_filetype = FileType.from_mime_type(content_type)
                element.metadata.filetype = out_filetype.mime_type if out_filetype else None
            else:
                element.metadata.filetype = file_type.mime_type

        return elements

    # -- handle PDF/Image partitioning separately because they have a lot of special-case
    # -- parameters. We'll come back to this after sorting out the other file types.
    if file_type == FileType.PDF:
        partition_pdf = partitioner_loader.get(file_type)
        elements = partition_pdf(
            filename=filename,
            file=file,
            url=None,
            infer_table_structure=infer_table_structure,
            strategy=strategy,
            languages=languages,
            hi_res_model_name=hi_res_model_name or model_name,
            extract_images_in_pdf=extract_images_in_pdf,
            extract_image_block_types=extract_image_block_types,
            extract_image_block_output_dir=extract_image_block_output_dir,
            extract_image_block_to_payload=extract_image_block_to_payload,
            starting_page_number=starting_page_number,
            **kwargs,
        )
        return augment_metadata(elements)

    if file_type.partitioner_shortname == "image":
        partition_image = partitioner_loader.get(file_type)
        elements = partition_image(
            filename=filename,
            file=file,
            url=None,
            infer_table_structure=infer_table_structure,
            strategy=strategy,
            languages=languages,
            hi_res_model_name=hi_res_model_name or model_name,
            extract_images_in_pdf=extract_images_in_pdf,
            extract_image_block_types=extract_image_block_types,
            extract_image_block_output_dir=extract_image_block_output_dir,
            extract_image_block_to_payload=extract_image_block_to_payload,
            starting_page_number=starting_page_number,
            **kwargs,
        )
        return augment_metadata(elements)

    # -- JSON is a special case because it's not a document format per se and is insensitive to
    # -- most of the parameters that apply to other file types.
    if file_type == FileType.JSON:
        if not is_json_processable(filename=filename, file=file):
            raise ValueError(
                "Detected a JSON file that does not conform to the Unstructured schema. "
                "partition_json currently only processes serialized Unstructured output.",
            )
        partition_json = partitioner_loader.get(file_type)
        elements = partition_json(filename=filename, file=file, **kwargs)
        return augment_metadata(elements)

    if file_type == FileType.NDJSON:
        if not is_ndjson_processable(filename=filename, file=file):
            raise ValueError(
                "Detected an NDJSON file that does not conform to the Unstructured schema. "
                "partition_json currently only processes serialized Unstructured output.",
            )
        partition_ndjson = partitioner_loader.get(file_type)
        elements = partition_ndjson(filename=filename, file=file, **kwargs)
        return augment_metadata(elements)

    # -- EMPTY is also a special case because while we can't determine the file type, we can be
    # -- sure it doesn't contain any elements.
    if file_type == FileType.EMPTY:
        return []

    # ============================================================================================
    #  ALL OTHER FILE TYPES
    # ============================================================================================

    partitioning_kwargs = copy.deepcopy(kwargs)
    partitioning_kwargs["detect_language_per_element"] = detect_language_per_element
    partitioning_kwargs["encoding"] = encoding
    partitioning_kwargs["infer_table_structure"] = infer_table_structure
    partitioning_kwargs["languages"] = languages
    partitioning_kwargs["starting_page_number"] = starting_page_number
    partitioning_kwargs["strategy"] = strategy
    partitioning_kwargs["extract_image_block_types"] = extract_image_block_types
    partitioning_kwargs["extract_image_block_to_payload"] = extract_image_block_to_payload

    partition = partitioner_loader.get(file_type)
    elements = partition(filename=filename, file=file, **partitioning_kwargs)
    return augment_metadata(elements)


def file_and_type_from_url(
    url: str,
    content_type: Optional[str] = None,
    headers: dict[str, str] = {},
    ssl_verify: bool = True,
    request_timeout: Optional[int] = None,
) -> tuple[io.BytesIO, FileType]:
    response = requests.get(url, headers=headers, verify=ssl_verify, timeout=request_timeout)
    file = io.BytesIO(response.content)

    if content_type := content_type or response.headers.get("Content-Type", None):
        content_type = content_type.split(";")[0].strip().lower()

    # -- non-None when response is textual --
    encoding = response.encoding

    filetype = detect_filetype(file=file, encoding=encoding, content_type=content_type)
    return file, filetype


def decide_table_extraction(
    filetype: Optional[FileType],
    skip_infer_table_types: list[str],
    pdf_infer_table_structure: bool,
) -> bool:
    doc_type = filetype.name.lower() if filetype else None

    if doc_type == "pdf":
        # For backwards compatibility. Ultimately we want to remove pdf_infer_table_structure
        # completely and rely exclusively on `skip_infer_table_types` for all file types.
        # Until then for pdf files we first check pdf_infer_table_structure and then update
        # based on skip_infer_tables.
        return pdf_infer_table_structure or doc_type not in skip_infer_table_types

    return doc_type not in skip_infer_table_types


class _PartitionerLoader:
    """Provides uniform helpful error when a partitioner dependency is not installed.

    Used by `partition()` to encapsulate coping with the possibility the Python environment it is
    executing in may not have all dependencies installed for a particular partitioner.

    Provides `.get()` to access partitioners by file-type, which raises when one or more
    dependencies for that partitioner are not installed.

    The error message indicates what extra needs to be installed to enable that partitioner. This
    avoids an inconsistent variety of possibly puzzling exceptions arising from much deeper in the
    partitioner when access to the missing dependency is first attempted.
    """

    # -- module-lifetime cache for partitioners once loaded --
    _partitioners: dict[FileType, Partitioner] = {}

    def get(self, file_type: FileType) -> Partitioner:
        """Return partitioner for `file_type`.

        Raises when one or more package dependencies for that file-type have not been
        installed. Also raises when the file-type is not partitionable.
        """
        if not file_type.is_partitionable:
            raise UnsupportedFileFormatError(
                f"Partitioning is not supported for the {file_type} file type."
            )

        # -- if the partitioner is not in the cache, load it; note this raises if one or more of
        # -- the partitioner's dependencies is not installed.
        if file_type not in self._partitioners:
            self._partitioners[file_type] = self._load_partitioner(file_type)

        return self._partitioners[file_type]

    def _load_partitioner(self, file_type: FileType) -> Partitioner:
        """Load the partitioner for `file_type` after verifying dependencies."""
        # -- verify all package dependencies are installed --
        for pkg_name in file_type.importable_package_dependencies:
            if not dependency_exists(pkg_name):
                raise ImportError(
                    f"{file_type.partitioner_function_name}() is not available because one or"
                    f" more dependencies are not installed. Use:"
                    f' pip install "unstructured[{file_type.extra_name}]" (including quotes)'
                    f" to install the required dependencies",
                )

        # -- load the partitioner and return it --
        assert file_type.is_partitionable  # -- would be a programming error if this failed --
        partitioner_module = importlib.import_module(file_type.partitioner_module_qname)
        return getattr(partitioner_module, file_type.partitioner_function_name)
